Tables display data from sample 238 individuals from Sims data set that sustained a lower extremity injury within 1 year of sustaining a concussion. To include more data, the information below does not contain information from the merged IMPACT data sample.
Table reflects 238 individuals from Sims data set with a diagnosed lower extremity injury following a concussion.
Descriptive statistics of time between concussion and lower extremity injury by gender
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_between_injury Female Male 93 121 0.204 195. 0.838 ns
To create plot, a new variable was created age_difference calculated as the difference between injury_2 age and injury_1 age. All differences are less than one year. The distribution is not normal with higher distribution at both tails.
To produce the model, I calculated the relationship between the age at second injury and the days between injuries. There appears to be a significant relationship, although the model only accounts for 5% of the variance. Overall, there is a 31.339 unit increase in the days between injury for every unit increase in the age at time of 2nd injury.
##
## Call:
## lm(formula = days_between_injury ~ age_2, data = age_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -218.66 -102.28 -30.33 113.82 222.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -331.996 126.135 -2.632 0.009059 **
## age_2 31.339 7.973 3.931 0.000112 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 118.5 on 231 degrees of freedom
## Multiple R-squared: 0.06269, Adjusted R-squared: 0.05863
## F-statistic: 15.45 on 1 and 231 DF, p-value: 0.000112
This section primarily explores the time between injuries for Football and Women’s Soccer as those are the two most common sports at time of concussion and lower extremity injury.
Table displayed statistics of the number of days between injuries for individuals who sustained their injuries playing football or women’s soccer.
Table displays the number of lower extremity injuries for individuals who played football and women’s soccer.
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_between_… Footba… Women's So… 66 20 -1.80 28.6 0.0823 ns
Schools with 20 or more individuals included.
Table displayed statistics of the number of days between injuries for individuals who attend Kalaheo High School, Maui High School, and Mililani High Schol.
The difference between the mean number of days between injury across the three high schools is significantly different. Students at Mililani High School, on average, experienced a much longer duration of time between injuries than students at Kalaheo High School and Maui High School.
## Df Sum Sq Mean Sq F value Pr(>F)
## school_2 2 122006 61003 4.227 0.0188 *
## Residuals 65 938025 14431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons using t tests with pooled SD
##
## data: school_analysis$days_between_injury and school_analysis$school_2
##
## Kalaheo High School Maui High School
## Maui High School 1.000 -
## Mililani High School 0.053 0.031
##
## P value adjustment method: bonferroni
Schools with 20 or more individuals included.
Table displayed statistics of the number of days between injuries for the sport level at the time of the individuals’ lower extremity injury.
Individuals playing varsity sports at the time of second injury have a significantly different longer duration of time between injuries.
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_between_… Junior Var… Varsi… 71 105 -3.48 165. 6.38e-4 ***
One individual removed from sample because of missing information for days since both injuries reducing sample total to 232 individauls. The number of days missed post-concussion appears to have severe outliers?
##
## Paired t-test
##
## data: days_missed_1 and days_missed_2
## t = 1.1298, df = 231, p-value = 0.2597
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -41.55266 153.25955
## sample estimates:
## mean of the differences
## 55.85345
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_missed_concussi… Female Male 92 121 -0.273 202. 0.785 ns
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_missed_lower_e… Female Male 92 121 1.90 143. 0.0594 ns
There does not appear to be a strong relationship between age and the number of days missed following either a concussion or lower extremity injury.
This section primarily explores the number of days missed following injuries for Football and Women’s Soccer as those are the two most common sports at time of concussion and lower extremity injury.
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_missed_co… Footba… Women's S… 66 19 1.87 74.9 0.0655 ns
## # A tibble: 1 x 9
## .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 days_missed_low… Footba… Women's S… 66 19 -0.868 21.2 0.395 ns